5,105 research outputs found
Toward the Automation of Diagnostic Conversation Analysis in Patients with Memory Complaints.
BACKGROUND: The early diagnosis of dementia is of great clinical and social importance. A recent study using the qualitative methodology of conversation analysis (CA) demonstrated that language and communication problems are evident during interactions between patients and neurologists, and that interactional observations can be used to differentiate between cognitive difficulties due to neurodegenerative disorders (ND) or functional memory disorders (FMD). OBJECTIVE: This study explores whether the differential diagnostic analysis of doctor-patient interactions in a memory clinic can be automated. METHODS: Verbatim transcripts of conversations between neurologists and patients initially presenting with memory problems to a specialist clinic were produced manually (15 with FMD, and 15 with ND). A range of automatically detectable features focusing on acoustic, lexical, semantic, and visual information contained in the transcripts were defined aiming to replicate the diagnostic qualitative observations. The features were used to train a set of five machine learning classifiers to distinguish between ND and FMD. RESULTS: The mean rate of correct classification between ND and FMD was 93% ranging from 97% by the Perceptron classifier to 90% by the Random Forest classifier.Using only the ten best features, the mean correct classification score increased to 95%. CONCLUSION: This pilot study provides proof-of-principle that a machine learning approach to analyzing transcripts of interactions between neurologists and patients describing memory problems can distinguish people with neurodegenerative dementia from people with FMD
Exploiting prompt learning with pre-trained language models for Alzheimer's Disease detection
Early diagnosis of Alzheimer's disease (AD) is crucial in facilitating
preventive care and to delay further progression. Speech based automatic AD
screening systems provide a non-intrusive and more scalable alternative to
other clinical screening techniques. Textual embedding features produced by
pre-trained language models (PLMs) such as BERT are widely used in such
systems. However, PLM domain fine-tuning is commonly based on the masked word
or sentence prediction costs that are inconsistent with the back-end AD
detection task. To this end, this paper investigates the use of prompt-based
fine-tuning of PLMs that consistently uses AD classification errors as the
training objective function. Disfluency features based on hesitation or pause
filler token frequencies are further incorporated into prompt phrases during
PLM fine-tuning. The decision voting based combination among systems using
different PLMs (BERT and RoBERTa) or systems with different fine-tuning
paradigms (conventional masked-language modelling fine-tuning and prompt-based
fine-tuning) is further applied. Mean, standard deviation and the maximum among
accuracy scores over 15 experiment runs are adopted as performance measurements
for the AD detection system. Mean detection accuracy of 84.20% (with std 2.09%,
best 87.5%) and 82.64% (with std 4.0%, best 89.58%) were obtained using manual
and ASR speech transcripts respectively on the ADReSS20 test set consisting of
48 elderly speakers.Comment: Accepted ICASSP 2023 (will update with IEEE vision later
Automatic Detection of Dementia and related Affective Disorders through Processing of Speech and Language
In 2019, dementia is has become a trillion dollar disorder. Alzheimer’s disease (AD) is a type of dementia in which the main observable symptom is a decline in cognitive functions, notably memory, as well as language and problem-solving. Experts agree that early detection is crucial to effectively develop and apply interventions and treatments, underlining the need for effective and pervasive assessment and screening tools. The goal of this thesis is to explores how computational techniques can be used to process speech and language samples produced by patients suffering from dementia or related affective disorders, to the end of automatically detecting them in large populations us- ing machine learning models. A strong focus is laid on the detection of early stage dementia (MCI), as most clinical trials today focus on intervention at this level. To this end, novel automatic and semi-automatic analysis schemes for a speech-based cogni- tive task, i.e., verbal fluency, are explored and evaluated to be an appropriate screening task. Due to a lack of available patient data in most languages, world-first multilingual approaches to detecting dementia are introduced in this thesis. Results are encouraging and clear benefits on a small French dataset become visible. Lastly, the task of detecting these people with dementia who also suffer from an affective disorder called apathy is explored. Since they are more likely to convert into later stage of dementia faster, it is crucial to identify them. These are the fist experiments that consider this task us- ing solely speech and language as inputs. Results are again encouraging, both using only speech or language data elicited using emotional questions. Overall, strong results encourage further research in establishing speech-based biomarkers for early detection and monitoring of these disorders to better patients’ lives.Im Jahr 2019 ist Demenz zu einer Billionen-Dollar-Krankheit geworden. Die Alzheimer- Krankheit (AD) ist eine Form der Demenz, bei der das Hauptsymptom eine Abnahme der kognitiven Funktionen ist, insbesondere des Gedächtnisses sowie der Sprache und des Problemlösungsvermögens. Experten sind sich einig, dass eine frühzeitige Erkennung entscheidend für die effektive Entwicklung und Anwendung von Interventionen und Behandlungen ist, was den Bedarf an effektiven und durchgängigen Bewertungsund Screening-Tools unterstreicht. Das Ziel dieser Arbeit ist es zu erforschen, wie computergest ützte Techniken eingesetzt werden können, um Sprach- und Sprechproben von Patienten, die an Demenz oder verwandten affektiven Störungen leiden, zu verarbeiten, mit dem Ziel, diese in großen Populationen mit Hilfe von maschinellen Lernmodellen automatisch zu erkennen. Ein starker Fokus liegt auf der Erkennung von Demenz im Frühstadium (MCI), da sich die meisten klinischen Studien heute auf eine Intervention auf dieser Ebene konzentrieren. Zu diesem Zweck werden neuartige automatische und halbautomatische Analyseschemata für eine sprachbasierte kognitive Aufgabe, d.h. die verbale Geläufigkeit, erforscht und als geeignete Screening-Aufgabe bewertet. Aufgrund des Mangels an verfügbaren Patientendaten in den meisten Sprachen werden in dieser Arbeit weltweit erstmalig mehrsprachige Ansätze zur Erkennung von Demenz vorgestellt. Die Ergebnisse sind ermutigend und es werden deutliche Vorteile an einem kleinen französischen Datensatz sichtbar. Schließlich wird die Aufgabe untersucht, jene Menschen mit Demenz zu erkennen, die auch an einer affektiven Störung namens Apathie leiden. Da sie mit größerer Wahrscheinlichkeit schneller in ein späteres Stadium der Demenz übergehen, ist es entscheidend, sie zu identifizieren. Dies sind die ersten Experimente, die diese Aufgabe unter ausschließlicher Verwendung von Sprache und Sprache als Input betrachten. Die Ergebnisse sind wieder ermutigend, sowohl bei der Verwendung von reiner Sprache als auch bei der Verwendung von Sprachdaten, die durch emotionale Fragen ausgelöst werden. Insgesamt sind die Ergebnisse sehr ermutigend und ermutigen zu weiterer Forschung, um sprachbasierte Biomarker für die Früherkennung und Überwachung dieser Erkrankungen zu etablieren und so das Leben der Patienten zu verbessern
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Artificial intelligence approaches to predicting and detecting cognitive decline in older adults: A conceptual review.
Preserving cognition and mental capacity is critical to aging with autonomy. Early detection of pathological cognitive decline facilitates the greatest impact of restorative or preventative treatments. Artificial Intelligence (AI) in healthcare is the use of computational algorithms that mimic human cognitive functions to analyze complex medical data. AI technologies like machine learning (ML) support the integration of biological, psychological, and social factors when approaching diagnosis, prognosis, and treatment of disease. This paper serves to acquaint clinicians and other stakeholders with the use, benefits, and limitations of AI for predicting, diagnosing, and classifying mild and major neurocognitive impairments, by providing a conceptual overview of this topic with emphasis on the features explored and AI techniques employed. We present studies that fell into six categories of features used for these purposes: (1) sociodemographics; (2) clinical and psychometric assessments; (3) neuroimaging and neurophysiology; (4) electronic health records and claims; (5) novel assessments (e.g., sensors for digital data); and (6) genomics/other omics. For each category we provide examples of AI approaches, including supervised and unsupervised ML, deep learning, and natural language processing. AI technology, still nascent in healthcare, has great potential to transform the way we diagnose and treat patients with neurocognitive disorders
Identification of Cognitive Decline from Spoken Language through Feature Selection and the Bag of Acoustic Words Model
Memory disorders are a central factor in the decline of functioning and daily
activities in elderly individuals. The confirmation of the illness, initiation
of medication to slow its progression, and the commencement of occupational
therapy aimed at maintaining and rehabilitating cognitive abilities require a
medical diagnosis. The early identification of symptoms of memory disorders,
especially the decline in cognitive abilities, plays a significant role in
ensuring the well-being of populations. Features related to speech production
are known to connect with the speaker's cognitive ability and changes. The lack
of standardized speech tests in clinical settings has led to a growing emphasis
on developing automatic machine learning techniques for analyzing naturally
spoken language. Non-lexical but acoustic properties of spoken language have
proven useful when fast, cost-effective, and scalable solutions are needed for
the rapid diagnosis of a disease. The work presents an approach related to
feature selection, allowing for the automatic selection of the essential
features required for diagnosis from the Geneva minimalistic acoustic parameter
set and relative speech pauses, intended for automatic paralinguistic and
clinical speech analysis. These features are refined into word histogram
features, in which machine learning classifiers are trained to classify control
subjects and dementia patients from the Dementia Bank's Pitt audio database.
The results show that achieving a 75% average classification accuracy with only
twenty-five features with the separate ADReSS 2020 competition test data and
the Leave-One-Subject-Out cross-validation of the entire competition data is
possible. The results rank at the top compared to international research, where
the same dataset and only acoustic features have been used to diagnose
patients
AI and Non AI Assessments for Dementia
Current progress in the artificial intelligence domain has led to the
development of various types of AI-powered dementia assessments, which can be
employed to identify patients at the early stage of dementia. It can
revolutionize the dementia care settings. It is essential that the medical
community be aware of various AI assessments and choose them considering their
degrees of validity, efficiency, practicality, reliability, and accuracy
concerning the early identification of patients with dementia (PwD). On the
other hand, AI developers should be informed about various non-AI assessments
as well as recently developed AI assessments. Thus, this paper, which can be
readable by both clinicians and AI engineers, fills the gap in the literature
in explaining the existing solutions for the recognition of dementia to
clinicians, as well as the techniques used and the most widespread dementia
datasets to AI engineers. It follows a review of papers on AI and non-AI
assessments for dementia to provide valuable information about various dementia
assessments for both the AI and medical communities. The discussion and
conclusion highlight the most prominent research directions and the maturity of
existing solutions.Comment: 49 page
A Review of Atrial Fibrillation Detection Methods as a Service
Atrial Fibrillation (AF) is a common heart arrhythmia that often goes undetected, and even if it is detected, managing the condition may be challenging. In this paper, we review how the RR interval and Electrocardiogram (ECG) signals, incorporated into a monitoring system, can be useful to track AF events. Were such an automated system to be implemented, it could be used to help manage AF and thereby reduce patient morbidity and mortality. The main impetus behind the idea of developing a service is that a greater data volume analyzed can lead to better patient outcomes. Based on the literature review, which we present herein, we introduce the methods that can be used to detect AF efficiently and automatically via the RR interval and ECG signals. A cardiovascular disease monitoring service that incorporates one or multiple of these detection methods could extend event observation to all times, and could therefore become useful to establish any AF occurrence. The development of an automated and efficient method that monitors AF in real time would likely become a key component for meeting public health goals regarding the reduction of fatalities caused by the disease. Yet, at present, significant technological and regulatory obstacles remain, which prevent the development of any proposed system. Establishment of the scientific foundation for monitoring is important to provide effective service to patients and healthcare professionals
A new speech corpus of super-elderly Japanese for acoustic modeling
The development of accessible speech recognition technology will allow the elderly to more easily access electronically stored information. However, the necessary level of recognition accuracy for elderly speech has not yet been achieved using conventional speech recognition systems, due to the unique features of the speech of elderly people. To address this problem, we have created a new speech corpus named EARS (Elderly Adults Read Speech), consisting of the recorded read speech of 123 super-elderly Japanese people (average age: 83.1), as a resource for training automated speech recognition models for the elderly. In this study, we investigated the acoustic features of super-elderly Japanese speech using our new speech corpus. In comparison to the speech of less elderly Japanese speakers, we observed a slower speech rate and extended vowel duration for both genders, a slight increase in fundamental frequency for males, and a slight decrease in fundamental frequency for females. To demonstrate the efficacy of our corpus, we also conducted speech recognition experiments using two different acoustic models (DNN-HMM and transformer-based), trained with a combination of data from our corpus and speech data from three conventional Japanese speech corpora. When using the DNN-HMM trained with EARS and speech data from existing corpora, the character error rate (CER) was reduced by 7.8% (to just over 9%), compared to a CER of 16.9% when using only the baseline training corpora. We also investigated the effect of training the models with various amounts of EARS data, using a simple data expansion method. The acoustic models were also trained for various numbers of epochs without any modifications. When using the Transformer-based end-to-end speech recognizer, the character error rate was reduced by 3.0% (to 11.4%) by using a doubled EARS corpus with the baseline data for training, compared to a CER of 13.4% when only data from the baseline training corpora were used
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